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Optimizing the production and distribution system of bioenergy villages

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  • Uhlemair, Harald
  • Karschin, Ingo
  • Geldermann, Jutta

Abstract

In bioenergy villages, local bioenergy plants are installed to supply electricity, which is fed into the national grid, and to heat households through a local heat distribution network. In this paper, a linear mathematical model, which economically optimizes local bioenergy production and distribution systems based on a given set of system components, is presented. The model simultaneously determines the optimal capacity of the system, the objects that should be connected to the heating network and the course of the network. Additionally, a combined heat and power (CHP) biogas plant builds the production system. The problem is modeled as a mixed integer linear program (MILP) and is applied to a village with n potential heat customers. This model offers the possibility of economically assessing various scenarios concerning different planning situations and optimizing the capacity planning for the biogas plant and the course of the district heating network.

Suggested Citation

  • Uhlemair, Harald & Karschin, Ingo & Geldermann, Jutta, 2014. "Optimizing the production and distribution system of bioenergy villages," International Journal of Production Economics, Elsevier, vol. 147(PA), pages 62-72.
    • Handle: RePEc:eee:proeco:v:147:y:2014:i:pa:p:62-72
    DOI: 10.1016/j.ijpe.2012.10.003
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    References listed on IDEAS

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    1. Lahdelma, Risto & Hakonen, Henri, 2003. "An efficient linear programming algorithm for combined heat and power production," European Journal of Operational Research, Elsevier, vol. 148(1), pages 141-151, July.
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    Cited by:

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    2. Sebastian Schär & Jutta Geldermann, 2021. "Adopting Multiactor Multicriteria Analysis for the Evaluation of Energy Scenarios," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    3. Li, Yu & Rezgui, Yacine & Zhu, Hanxing, 2017. "District heating and cooling optimization and enhancement – Towards integration of renewables, storage and smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 281-294.
    4. Choi, Chul Hun & Eun, Joonyup & Cao, Jinjian & Lee, Seokcheon & Zhao, Fu, 2018. "Global strategic level supply planning of materials critical to clean energy technologies – A case study on indium," Energy, Elsevier, vol. 147(C), pages 950-964.
    5. Lerche, Nils & Wilkens, Ines & Schmehl, Meike & Eigner-Thiel, Swantje & Geldermann, Jutta, 2019. "Using methods of Multi-Criteria Decision Making to provide decision support concerning local bioenergy projects," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    6. Bekkering, J. & Hengeveld, E.J. & van Gemert, W.J.T. & Broekhuis, A.A., 2015. "Designing a green gas supply to meet regional seasonal demand – An operations research case study," Applied Energy, Elsevier, vol. 143(C), pages 348-358.
    7. Razm, Sobhan & Brahimi, Nadjib & Hammami, Ramzi & Dolgui, Alexandre, 2023. "A production planning model for biorefineries with biomass perishability and biofuel transformation," International Journal of Production Economics, Elsevier, vol. 258(C).
    8. De Meyer, Annelies & Cattrysse, Dirk & Van Orshoven, Jos, 2015. "A generic mathematical model to optimise strategic and tactical decisions in biomass-based supply chains (OPTIMASS)," European Journal of Operational Research, Elsevier, vol. 245(1), pages 247-264.

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